Debate Pattern

Multiple agents argue assigned positions across rounds, then a judge renders a final decision.

Best for: High-stakes decisions, buy-vs-build, investment theses, strategy trade-offs.
LLM calls: D × R (debaters × rounds) + 1 judge.

---

Sequence Diagram

sequenceDiagram
    participant U as User
    participant B as Bull Agent
    participant Be as Bear Agent
    participant J as Judge

    U->>B: Argue BUY
    U->>Be: Argue SELL

    rect rgb(230, 245, 255)
        Note over B,Be: Round 1 — Opening arguments
        B->>J: "Strong earnings growth justifies premium..."
        Be->>J: "P/E ratio unsustainably high relative to peers..."
    end

    rect rgb(255, 245, 230)
        Note over B,Be: Round 2 — Rebuttals
        B->>J: "Growth trajectory intact, P/E justified by optionality..."
        Be->>J: "Growth slowing, competition intensifying in core market..."
    end

    J-->>U: "Decision: HOLD — valid points on both sides"

---

Use Case 1 — Investment Decision (Gemini)

import asyncio
from pyagent_patterns.base import Agent
from pyagent_patterns.resolution import Debate
from pyagent_providers import GeminiLLM, AnthropicLLM

debate = Debate(
    debaters=[
        Agent(
            "bull",
            GeminiLLM("gemini-2.5-pro"),
            system_prompt="You are a bullish investment analyst. Argue that this investment "
                          "should be made. Use specific financial metrics, growth rates, and "
                          "comparable valuations. Anticipate and pre-emptively rebut bear arguments. "
                          "Support every claim with data.",
        ),
        Agent(
            "bear",
            GeminiLLM("gemini-2.5-pro"),
            system_prompt="You are a bearish investment analyst. Argue that this investment "
                          "should NOT be made. Focus on: overvaluation, execution risk, "
                          "competitive threats, and downside scenarios. "
                          "Rebut the bull case with specific counter-evidence.",
        ),
    ],
    judge=Agent(
        "judge",
        AnthropicLLM("claude-sonnet-4-20250514"),
        system_prompt="You are a senior portfolio manager reviewing this debate. "
                      "Evaluate argument quality, use of evidence, and logical consistency. "
                      "Identify which side had the stronger case for each key point. "
                      "Render a final verdict: BUY / HOLD / SELL with conviction level. "
                      "Explain which arguments were decisive.",
    ),
    rounds=2,
    positions=["BUY", "SELL"],
)

result = asyncio.run(debate.run(
    "Should we invest in Nvidia at $3.2T market cap, 45x forward P/E, "
    "with data center revenue growing 150% YoY but competition from AMD and custom silicon?"
))
print(result.output)
print(f"Rounds: {result.metadata['rounds']}")
print(f"Arguments logged: {len(result.metadata['debate_log'])}")
print(f"Cost: ${result.cost_estimate:.4f}")

---

Use Case 2 — Build vs Buy Decision (OpenAI + Anthropic)

from pyagent_providers import OpenAILLM

build_buy = Debate(
    debaters=[
        Agent(
            "build_advocate",
            OpenAILLM("gpt-4o"),
            system_prompt="Argue that we should BUILD this capability in-house. "
                          "Address: control, customisation, long-term cost, IP ownership, "
                          "competitive differentiation, and team capability development.",
        ),
        Agent(
            "buy_advocate",
            OpenAILLM("gpt-4o"),
            system_prompt="Argue that we should BUY (use a vendor/SaaS). "
                          "Address: speed to market, TCO vs build cost, vendor expertise, "
                          "maintenance burden, and opportunity cost of engineering time.",
        ),
    ],
    judge=Agent(
        "decision_maker",
        AnthropicLLM("claude-sonnet-4-20250514"),
        system_prompt="You are a VP of Engineering making a final recommendation. "
                      "Evaluate both cases. Identify what information is missing. "
                      "Give a recommendation: BUILD / BUY / HYBRID, with a 3-bullet rationale.",
    ),
    rounds=2,
    positions=["BUILD", "BUY"],
)

result = asyncio.run(build_buy.run(
    "Should we build our own RAG pipeline or use a managed solution like Pinecone + LangChain? "
    "Team: 4 engineers. Timeline: 6 weeks to production. "
    "Scale: 10M documents, 50k queries/day."
))
print(result.output)

---

Use Case 3 — Technical Architecture Debate (LiteLLM multi-provider)

from pyagent_providers import LiteLLM

arch_debate = Debate(
    debaters=[
        Agent(
            "microservices_advocate",
            LiteLLM("gpt-4o"),
            system_prompt="Argue for a microservices architecture. Address scalability, "
                          "team autonomy, independent deployment, and fault isolation.",
        ),
        Agent(
            "monolith_advocate",
            LiteLLM("anthropic/claude-sonnet-4-20250514"),
            system_prompt="Argue for a modular monolith. Address simplicity, "
                          "developer experience, deployment complexity, and premature optimisation.",
        ),
    ],
    judge=Agent(
        "architect",
        LiteLLM("gemini/gemini-2.5-pro"),
        system_prompt="Evaluate the architecture debate. Consider the team size, scale, "
                      "and constraints. Give a specific recommendation with trade-offs.",
    ),
    rounds=2,
    positions=["MICROSERVICES", "MONOLITH"],
)

result = asyncio.run(arch_debate.run(
    "Architecture decision for a new B2B SaaS platform: 5-engineer team, "
    "18-month runway, targeting 1k enterprise customers at launch."
))

---

OTel Trace Output

Trace: pyagent.pattern.debate (9.8s, $0.038)
├── Round 1
│   ├── pyagent.agent.bull (2.4s, gemini-2.5-pro)
│   └── pyagent.agent.bear (2.7s, gemini-2.5-pro)
├── Round 2
│   ├── pyagent.agent.bull (2.1s, gemini-2.5-pro)
│   └── pyagent.agent.bear (1.9s, gemini-2.5-pro)
└── pyagent.agent.judge (0.7s, claude-sonnet-4-20250514)

---

When to Use

Condition	Recommendation
Decision requires examining opposing viewpoints	✅ Use Debate
Stakes are high — adversarial testing adds value	✅ Use Debate
Answer is factual, not arguable	❌ Single-shot call
Budget is tight	❌ Many LLM calls — consider Cross-Reflection
You need nuanced open-ended output	❌ Use Evaluator-Optimizer

---

See Also

• Voting — parallel agents that vote independently rather than debate
• Cross-Reflection — generate + review rather than argue + judge
• Fan-Out / Fan-In — parallel independent analysis without adversarial framing